Discovery & Use-Case Prioritization
We map your conversations by cost and volume and set the accuracy and deflection targets up front. Most vendors automate the easy conversations; we go after the expensive ones first.
Conversational AI that survives production, not just the demo
Most conversational AI looks great in the demo and breaks the moment it meets real customers — messy phrasing, brittle integrations, a support team that won't trust the bot. Banao engineers the last 80% vendors skip: agents grounded in your own data, wired into your CRM and ticketing, governed for accuracy, and monitored once they're live. We've run conversational AI for a telecom carrier in crisis, a UAE national authority, and our own 300-person operation since 2017 — so you're not the beta tester.
The gap between a chatbot demo and a deployed agent
Buying conversational AI is easy. Operating it is where projects fail. An agent that answers FAQs in a sandbox still has to survive ambiguous phrasing, multi-turn context, escalation to humans, data privacy, and a brand voice your customers recognize. Banao builds for that reality — NLU and retrieval grounded in your data, deterministic fallbacks on high-risk topics, human-in-the-loop review, and telemetry that shows exactly what the agent says and where it hands off. The result is an agent your support team actually keeps switched on.
What we engineer into your conversational AI
Every build starts from your highest-cost conversations — the ones flooding your support queue or stalling your funnel — and works backward to the agent, the integrations, and the guardrails that resolve them in production.
Scope your virtual agent
Conversational AI Strategy & Use-Case Mapping
We pinpoint the conversations worth automating by cost and volume, then design the flows and choose between rule-based, RAG, and fine-tuned approaches — and set the accuracy bar before a line of code ships.
Chat Agents for Web, WhatsApp & Messenger
Production chat agents for support, qualification, and self-service on the channels your customers already use — grounded in your knowledge base so answers come from your business, not a generic model.
Voice Agents & Callbot Automation
Voice callbots with natural-language understanding for phone lines, IVR, and in-app — the same architecture we built for Elisa to absorb tens of thousands of calls a day during a national crisis.
RAG Knowledge Agents
Agents that answer from your documents, tickets, and policies using retrieval-augmented generation and vector search — so responses stay current without retraining the whole model.
Multilingual & Bilingual Agents
Agents that hold a conversation across languages — including the English/Arabic bilingual chatbot we shipped for the UAE's national CSR authority, Majra — for teams serving GCC and global users.
CRM & Ticketing Integration
We wire agents into Salesforce, Zendesk, e-commerce, and internal systems so conversations trigger real actions — orders, tickets, account changes — not dead-end replies.
Intent, Sentiment & Escalation Logic
NLP that reads intent and sentiment to route the right conversations to humans at the right moment — so the agent deflects volume without trapping a frustrated customer in a loop.
Custom Model Training & Evaluation
We fine-tune and evaluate NLU and speech models on your domain data, with a measurable accuracy benchmark and a regression suite — so quality is provable, not assumed.
Where we deploy conversational AI
Retail & E-commerce
Deflect order-status and returns volume, recover carts, and guide product discovery — conversational commerce is where retail sees the fastest AI payback.
Telecom & Utilities
Absorb call surges, automate troubleshooting, and route service requests — the Elisa callbot handled tens of thousands of daily contacts when manual support couldn't scale.
Healthcare & Wellness
Triage queries, automate scheduling, and surface patient information through agents built with privacy and compliance controls in the architecture — not bolted on after.
Banking & Finance
Resolve account, onboarding, and FAQ conversations through agents with audit trails and role-based access controls fit for regulated environments.
Government & Public Sector
Bilingual citizen and employee agents over secure internal knowledge — the model behind Majra's English/Arabic intranet assistant for the UAE's national CSR authority.
EdTech & Learning
Tutoring and support agents that answer from approved course material and hand off to staff when a question needs a human.
Conversational AI we've shipped
Voice AI | Customer Support AI
Elisa AI Callbot
When a national crisis flooded Elisa, a major telecom carrier, with more calls than human agents could answer, their legacy support system buckled. Banao engineered an AI voice callbot with natural-language understanding, wired live into Elisa's CRM with automated routing, escalation, and self-learning models that improved with every interaction. It absorbed tens of thousands of contacts a day, cut waiting times, and turned a slow legacy queue into an agile, data-driven operation.
Majra
The UAE's national CSR and sustainability authority, Majra, was losing staff hours to scattered bilingual content and had no unified way to train or engage employees. Banao built an English/Arabic AI chatbot grounded in internal data, integrated with a SharePoint intranet and a gamified e-learning hub with role-based workflow automation. Hunting for documents across platforms became a single conversation, and employee engagement rose through the learning portal.
Evo AI-Powered Agent
EvoAI needed to turn a basic chatbot into a platform where users could spin up custom AI agents on their own data and get reliable, real-time answers across domains. Banao engineered an LLM-powered agent platform on FastAPI, Llama, and Groq that ingests uploaded datasets into embeddings and generates MongoDB queries at runtime, with synonym and context recognition for accuracy. Users now build industry-ready agents from their own data and query live information conversationally, with confidence in the results.
How we take conversational AI to production
Conversation & Data Design
We design dialogue flows and assemble the grounding data — knowledge bases, tickets, transcripts — the agent answers from. An agent is only as trustworthy as its data, so we treat this as engineering, not scripting.
Model Selection & Grounding
We choose between rule-based, RAG, and fine-tuned models per use case and ground them in your domain. The wrong approach is why bots either hallucinate or feel robotic — we match the method to the risk.
Testing & Human-in-the-Loop QA
We test against real phrasing, adversarial inputs, and fallback paths with human review before launch. The failure mode that kills trust is a confident wrong answer, so we engineer for a graceful 'I don't know' over false confidence.
Deployment & Integration
We deploy into your channels and wire the agent into CRM, ticketing, and backend systems with monitoring dashboards. A reply that can't trigger an action is a dead end, so integration is the deliverable — not an afterthought.
Monitoring & Continuous Improvement
We watch live transcripts, retrain on real failures, and tune flows on a cadence. Conversational AI degrades as your products and customers change — we run it like a system, not a project that ends at launch.
What teams running our conversational AI say
Meera S.
Head of Customer Experience, TeleLink
David Lim
Product Manager, EduBot
Support at scale, human feel
We brought Banao in when our support queue was drowning. They automated the high-volume conversations first and kept escalation paths clean, so deflection went up without CSAT dropping. They understand dialogue design and the operational reality behind it.
Where we're located
Let's Build Something Great Together. 🤝
Here is what you will get for submitting your contact details.
45 minutes of free consultation- A strict non-disclosure agreement
- Free market & competitive analysis
- Suggestions on revenue models & planning
- Detailed feature list document
- No obligation proposal
- Action plan to kick start your project
Frequently asked questions
How is conversational AI different from the rule-based chatbot we already tried?
A rule-based bot follows scripted decision trees and breaks the moment a user phrases something it didn't anticipate. Conversational AI uses NLU and retrieval to understand intent and answer from your live data — and, just as important, knows when to escalate to a human instead of guessing.
How do you stop the agent from giving wrong or made-up answers?
We ground answers in your own content with retrieval-augmented generation, add deterministic fallbacks on high-risk topics, and run human-in-the-loop QA before launch. Then we monitor live transcripts so a wrong answer becomes a fix, not a recurring failure.
Can it work in our languages and plug into our existing systems?
Yes. We build multilingual and bilingual agents — including English/Arabic for GCC clients — and integrate with CRM, ticketing, e-commerce, and internal knowledge bases so conversations trigger real actions, not dead-end replies.
Who owns the data, models, and code — and is it secure?
You own all of it: the code, the trained models, and the conversation data. We work under NDA, apply encryption and role-based access, and sign DPAs for regulated industries like healthcare, finance, and government.
How do we know it's actually working after launch?
We instrument every agent with telemetry — deflection rate, escalation rate, accuracy, and full transcripts — and review them on a cadence to retrain on real failures. You get a dashboard, not a black box.
How long does it take, and what does it cost?
A working pilot typically ships in 3–6 weeks; larger integrated deployments run 2–3 months depending on systems and compliance. We scope a fixed price after a short discovery session rather than quote blind — book a 45-minute call and we'll map your use cases and a realistic number.